Hi, First, some quick terminology I am using: Fixed effects = model with unit dummy variables Random effects = model without unit dummy variables, integrating unit-level variance out of likelihood I am confused about the difference between the multilevel modeling framework of lmer() and a "fixed effects" model with unit dummy variables. Say I had the following model with individual-level variable x, estimated with lmer(): model1 <- lmer(y ~ x + (1|unit)) How is model1 different from this?: model2 <- lm(y ~ x + factor(unit)) I was under the impression that the lmer() function was a "random effects" estimator as I have defined above. But if you use the command ranef(model1), R returns unit-specific deviations from the intercept. This seems to be more in line with the fixed effects estimator that returns intercept estimates for each unit. Question 2: Why is it that I can add in unit-level predictors into lmer(), which I cannot do in a standard fixed effects model with unit dummies? Thank you. -- View this message in context: http://www.nabble.com/lmer%28%29-vs.-%22fixed-effects%22-regression-tp25577800p25577800.html Sent from the R help mailing list archive at Nabble.com.